Batteries have been commonly used in many applications for years to provide critical electrical energy to a wide range of applications. For example, batteries are widely used in the automotive industry to provide cranking energy to start an engine, to energize accessory loads in a vehicle and to supply electricity to electric motors that provide the motive force for the vehicle. Batteries are also used in many industrial applications to energize indoor vehicle equipment or to provide back-up/standby power to critical equipment such as computers or telecommunications gear in the event of a utility power failure.
Because of the ubiquitous use of batteries in so many applications, it has become important to understand the health of the battery so that the reliability of the primary application is assured. A number of battery testing and monitoring methods have been developed over the past 50 or more years to provide critical information about battery health. The most basic methods include measuring specific gravity of the electrolyte, or measuring voltage and also voltage under a particular load. Measurements of voltage under load are interpreted by the device or by the user of the device to determine the health of the battery.
These methods have been largely replaced over the past 30 years by electronic testing methods that are generally referred to as “ohmic” testing methods, wherein the internal resistance of the battery is measured and compared to a reference value. Exemplary ohmic battery testing methods are described in U.S. Pat. Nos. 6,704,629 and 7,212,006 to Huang, U.S. Pat. No. 5,744,962 to Alber, U.S. Pat. No. 3,873,911 to Champlin, and many others.
Generally, these ohmic test methods are considered to more efficiently and more reliably indicate battery health than traditional load testing methods or specific gravity testing methods. However, ohmic test devices are generally quite costly compared to the equipment required for the more traditional test methods.
Furthermore, ohmic testers as commercially implemented are “integrated”, that is, they perform both the measurement and analysis functions. Such integrated battery testers are provided, for example, with test leads to be connected to the battery so that raw data parameters can be measured, and are further provided with a keypad or the like whereby the user can input several items of data to the device concerning the specifications of a battery under test, and with a microprocessor, memory, a stored program, and the like so that the parameters can be properly analyzed and a useful result provided to the user.
The items of data input to a battery tester are referred to herein as “context parameters”. Input of these context parameters to the tester is needed in order that the measured parameters of a particular battery under test can be meaningfully compared to nominal specifications of a similar battery in good condition. The context parameters are used to select an appropriate algorithm to calculate battery health and condition with reasonable accuracy. Without such context parameters, the measured values derived from a battery are insufficient to determine battery health or condition.
For example, ohmic testers typically perform the step of measuring the battery's “open-circuit voltage” (“OCV”), that is, the voltage across the battery when disconnected from external loads. Suppose an OCV of 6.4 V is measured. Absent knowledge of the battery's nominal voltage rating, it is impossible to determine whether an OCV of 6.4V is indicative of a good or bad battery, or a charged or discharged battery. If the device (or the user) knows that the battery should have a fully charged voltage of 12.6V, an OCV of 6.4V indicates that the battery is substantially discharged and possibly defective. However, if the battery context parameters indicate that the battery is nominally a 6V battery, an OCV of 6.4V indicates that it can be considered to be fully charged. (It will be appreciated by those of skill in the art that this alone does not establish that the battery is in good condition, merely that it is fully charged. Additional measurements and context information are required to determine the condition of the battery.)
When using ohmic testing to determine the condition of a battery, certain context parameters must be used to determine a qualitative test result from the raw data measured by the tester. Such battery context parameters may include, for example, nominal or expected battery voltage, nominal battery capacity or rating, battery rating system, battery construction type, nominal battery size, battery manufacturer, battery chemistry, battery age, battery temperature, battery pack configuration, battery location, test point location, terminal type, terminal material, battery model, etc. In existing commercial battery testers, such context parameters are typically determined by the user and appropriate values are sequentially entered by the user into the tester.
More specifically, depending upon the test application, a critical few or many context parameters must be entered by the user before a test can be performed and qualitative results determined. This necessitates that the tester comprise a user interface for entering these values. The user interface of the typical integrated battery tester is a display screen of some type, and a keyboard, keypad, or set of buttons that the user presses in sequence to enter or select values, typically in response to prompts displayed on the screen. Through this interface the user selects appropriate context values for the test, initiates the test, and performs post-test operations, such as viewing test results, and printing or saving the results. The user interfaces provided in current commercial battery testers are typically cumbersome, requiring an excessive number of button presses, so as to be time consuming to operate. Further, current user interfaces can be confusing to operate without instructions. Both can lead to user errors and omission of critical context information.
More particularly, the process of determining and inputting context parameters into a battery tester is subject to error and uncertainty. When faced with the prompt to enter battery context parameters, the user must understand which parameters are required. The user must be able to determine those parameters from looking at the battery or other available information. The user must enter the parameters accurately and completely. Often there is simply not sufficient information available about the battery for the user to determine the basic and critical context parameters. This problem is particularly noticeable in the case of Absorbed Glass Mat (AGM) batteries. The electrical performance characteristics, and particularly ohmic performance, of AGM batteries can vary substantially from model to model and manufacturer to manufacturer. Accurate ohmic testing of AGM batteries relies on an accurate and precise understanding of the particular ohmic context parameter for that battery model. However, such information is not provided on the label of the battery. Additionally, even if the ohmic characteristic was known for a particular battery model, the same battery model may be sold in the market place under many different brands, such that it is nearly impossible to determine the origin and proper context parameters for the battery because the battery is insufficiently identified.
In practice, because of the difficulty of accurately and specifically identifying batteries, context parameters are often generalized such that broad categories of batteries are grouped together and a general context parameter setting is used for the entire category. For example, the context parameter ‘battery construction type’ has been generalized to consist of three basic (and visually identifiable) groups of batteries, referred to as WET, AGM FLAT, and AGM Spiral. The user will select the closest of these battery type groups based on visual inspection of the battery. However, the ultimate accuracy of the analysis is severely constrained, because every battery has particular battery type characteristics that, if identified correctly, could be used to determine a much more accurate battery test result. Ideally, a unique construction type parameter would be provided for each battery model that reflects the very specific characteristics of that battery.
The identification of specific battery type characteristics is however very difficult. It is not practical or convenient for battery manufacturers to indicate specific battery type details on the battery label. Rather, only general specifications of interest to the purchaser are included on the label. Additionally, often the same battery is marketed under different brands unrelated to the original manufacturer, such that identification of specific battery characteristics is nearly impossible by looking at the label. Further, as noted above, even if the precise context parameters are identifiable by the user, the input of that information into the tester is critical and often performed incorrectly. Commonly the technician using the testing device fails to properly enter the information, such that one or more context parameters are omitted or entered incorrectly. Incomplete or incorrect entry of context parameters leads to erroneous test results.
Presuming the context parameters have been entered into the battery tester, the user initiates a battery test. The tester will measure certain raw data parameters from the battery. Typically, only two or three raw parameters are measured, such as voltage and an ohmic value, and occasionally temperature. However, many other parameters are measurable and could be useful in determining the health of the battery, including: capacitance, reactance, inductance, fluid levels, specific gravity of the electrolyte, current, etc., and are accordingly within the scope of the invention. These raw measured parameters are then used in a complex set of algorithms to determine qualitative results as a function of the entered context parameters. These qualitative results indicate the condition of the battery and may include, for example, estimated cold cranking amps, state of charge, state of health, remaining battery life, cranking health, and others. These determined qualitative values are then compared to the context parameters input by the user in order to determine a conclusion, typically a pass or fail condition. These calculations are performed using a microprocessor and are typically displayed on the screen of the user interface, and may be printed or saved in a memory on the device for future reference. In the vehicle battery context, it is also known to start the engine of the vehicle and measure the minimum voltage across the battery during cranking, indicative of battery health, and to measure the voltage and current ripple after the engine starts, indicative of alternator performance.
In more complex battery testing environments, for example, testing a parallel pack of batteries, or testing and comparing multiple batteries in a series string of batteries, the tester hardware and firmware must accommodate a lengthy and complex test sequence, e.g., instructing the user to perform multiple steps and then storing and comparing qualitative and quantitative results from numerous tests.
As battery testing has evolved and the results of battery tests are becoming more important in the process of quality improvement and battery warranty management, it is desirable to be able to extract the test reports or test records from the test device so that they may be collected and further analyzed or shared, or combined with other information to provide management reports, for example. This process of saving and exchanging information is often cumbersome and expensive. Normally, providing this capability will require memory on board the device to store a large number of test records, along with an interface to allow the files stored by the device to be transferred to another device, for example, to a computer system for tracking data for a fleet of vehicles. Methods for this transfer of stored data to another device include use of removable memory cards, or of on-board memory that is readable through a data port, such as a serial or “USB” port connected to a computer. Alternative methods include a “WiFi”, “Bluetooth” or other radio or infrared connection between the tester and a remote device such that test reports can be sent to a network location.
Thus, it can be seen that while the testing function of a battery tester per se is comparatively simple in that only a very few raw data parameters can possibly be determined from the battery itself, commercially available battery testers have become very complex and expensive in order to provide useful qualitative results that indicate battery condition and provide actionable recommendations for a wide variety of battery types, and furthermore in order to provide the ability of securing and exchanging results and reports, printing results, sending results to a remote device for further analysis, and the like.
Similar concerns apply in the case of other forms of specialized integrated test equipment, for example, specialized equipment for reading diagnostic data from an automobile's “on-board diagnostic” (OBD) port, equipment for verifying proper performance of telecommunications facilities, and many others. More specifically, there are numerous examples of testers of various kinds wherein the actual collection of data, downloading of status codes, performance of simple tests and the like is a simple matter as compared to analysis of the test results, receiving needed context data, providing a user interface, communication of the results to other devices, and the like.